| Abstract: | We define the maximum-relevance weighted likelihood estimator (MREWLE) using the relevance-weighted likelihood function introduced by Hu and Zidek (1995). Furthermore, we establish the consistency of the MREWLE under a wide range of conditions. Our results generalize those of Wald (1948) to both nonidentically distributed random variables and unequally weighted likelihoods (when dealing with independent data sets of varying relevance to the inferential problem of interest). Asymptotic normality is also proven. Applying these results to generalized smoothing model is discussed. |
